The document analyzes the decline of the Italian company Saras S.p.A. It begins with an overview of the company and analysis of its stock performance from 2007-2012, noting a decline coinciding with the 2008 financial crisis. It then performs a change point analysis that identifies a reduction in stock volatility in January 2012 following announcements denying rumors of delisting. The document next discusses option valuation models, focusing on the Black-Scholes model for pricing European stock options under assumptions such as stock prices following a continuous random process.
Ch & Cie GRA - Risk management in exotic derivatives tradingC Louiza
This document discusses risk management challenges with exotic derivatives, using interest rate structured derivatives desks as an example. It notes that desks accumulated large exposures to spread range accrual products tied to interest rate spreads. These products had discontinuous payoffs that became difficult to manage when the EUR swap curve suddenly inverted in June 2008. The inversion caused the desks' gamma exposures to flip, resulting in large losses over a short period. More robust risk management techniques could have helped avoid such losses by better monitoring exposures to barrier levels and managing risk concentrations.
White paper risk management in exotic derivatives trading - ch cie gra -- vdefAugustin Beyot
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses how efficient risk management using techniques like stress testing and limiting risk concentrations could have helped banks avoid such losses from unhedgeable risks in exotic structured products.
The document provides an overview of fundamental analysis and technical analysis techniques used in security analysis. It discusses various fundamental analysis approaches like economy analysis, industry analysis, and company analysis. It also covers technical analysis indicators like Dow Theory, Elliott Wave Principle, chart types, chart patterns, and moving averages. Finally, it provides a brief introduction to the efficient market theory which states that security prices reflect all available information.
The document provides an introduction to options pricing, including definitions of options contracts and key models used to determine theoretical option value, such as the Black-Scholes model. It discusses how options give the holder the right to buy or sell the underlying asset at a specified price. Models use known variables like underlying price and implied volatility to calculate theoretical option value over time. The Black-Scholes model, developed in 1973, is one of the most widely used options pricing models.
This document discusses numerical methods for valuing American put options. It begins with background on vanilla and exotic options, then covers American options and the optimal exercise boundary problem. Three numerical methods for valuing American put options are examined: binomial methods, finite difference methods, and Monte Carlo methods. The document compares the three methods and discusses areas for further research.
This document presents the pricing, hedging, and risk management of a portfolio containing two basket Asian options. It first describes the contractual terms of the two options and the stocks in their respective baskets. It then outlines the necessary preliminary computations, including bootstrapping the discount curve, determining reset dates, and analyzing historical stock data. The document models the stock price dynamics under both Normal Inverse Gaussian (NIG) and Geometric Brownian Motion (GBM) and prices the options using Monte Carlo simulation. It further examines the Greeks, hedging strategy, and calculates Value at Risk both with and without hedging using different approximations.
This document is a user manual that explains an automated weekly report on how non-commercial traders are positioned in currency futures based on the Commitment of Traders (CoT) report. The report includes an email summary highlighting key currency pairs and an Excel file with detailed data on positioning across currency pairs historically and currently. The manual defines key terms used in the report and explains the methodology for calculating implied positions in currency pairs not directly reported in the CoT data.
Ch & Cie GRA - Risk management in exotic derivatives tradingC Louiza
This document discusses risk management challenges with exotic derivatives, using interest rate structured derivatives desks as an example. It notes that desks accumulated large exposures to spread range accrual products tied to interest rate spreads. These products had discontinuous payoffs that became difficult to manage when the EUR swap curve suddenly inverted in June 2008. The inversion caused the desks' gamma exposures to flip, resulting in large losses over a short period. More robust risk management techniques could have helped avoid such losses by better monitoring exposures to barrier levels and managing risk concentrations.
White paper risk management in exotic derivatives trading - ch cie gra -- vdefAugustin Beyot
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses how efficient risk management using techniques like stress testing and limiting risk concentrations could have helped banks avoid such losses from unhedgeable risks in exotic structured products.
The document provides an overview of fundamental analysis and technical analysis techniques used in security analysis. It discusses various fundamental analysis approaches like economy analysis, industry analysis, and company analysis. It also covers technical analysis indicators like Dow Theory, Elliott Wave Principle, chart types, chart patterns, and moving averages. Finally, it provides a brief introduction to the efficient market theory which states that security prices reflect all available information.
The document provides an introduction to options pricing, including definitions of options contracts and key models used to determine theoretical option value, such as the Black-Scholes model. It discusses how options give the holder the right to buy or sell the underlying asset at a specified price. Models use known variables like underlying price and implied volatility to calculate theoretical option value over time. The Black-Scholes model, developed in 1973, is one of the most widely used options pricing models.
This document discusses numerical methods for valuing American put options. It begins with background on vanilla and exotic options, then covers American options and the optimal exercise boundary problem. Three numerical methods for valuing American put options are examined: binomial methods, finite difference methods, and Monte Carlo methods. The document compares the three methods and discusses areas for further research.
This document presents the pricing, hedging, and risk management of a portfolio containing two basket Asian options. It first describes the contractual terms of the two options and the stocks in their respective baskets. It then outlines the necessary preliminary computations, including bootstrapping the discount curve, determining reset dates, and analyzing historical stock data. The document models the stock price dynamics under both Normal Inverse Gaussian (NIG) and Geometric Brownian Motion (GBM) and prices the options using Monte Carlo simulation. It further examines the Greeks, hedging strategy, and calculates Value at Risk both with and without hedging using different approximations.
This document is a user manual that explains an automated weekly report on how non-commercial traders are positioned in currency futures based on the Commitment of Traders (CoT) report. The report includes an email summary highlighting key currency pairs and an Excel file with detailed data on positioning across currency pairs historically and currently. The manual defines key terms used in the report and explains the methodology for calculating implied positions in currency pairs not directly reported in the CoT data.
Solution to Black-Scholes P.D.E. via Finite Difference Methods (MatLab)Fynn McKay
Simple implementable of Numerical Analysis to solve the famous Black-Scholes P.D.E. via Finite Difference Methods for the fair price of a European option.
Statistical Arbitrage
Pairs Trading, Long-Short Strategy
Cyrille BEN LEMRID

1 Pairs Trading Model 5
1.1 Generaldiscussion ................................ 5 1.2 Cointegration ................................... 6 1.3 Spreaddynamics ................................. 7
2 State of the art and model overview 9
2.1 StochasticDependenciesinFinancialTimeSeries . . . . . . . . . . . . . . . 9 2.2 Cointegration-basedtradingstrategies ..................... 10 2.3 FormulationasaStochasticControlProblem. . . . . . . . . . . . . . . . . . 13 2.4 Fundamentalanalysis............................... 16
3 Strategies Analysis 19
3.1 Roadmapforstrategydesign .......................... 19 3.2 Identificationofpotentialpairs ......................... 19 3.3 Testingcointegration ............................... 20 3.4 Riskcontrolandfeasibility............................ 20
4 Results
22
2
Contents

Introduction
This report presents my research work carried out at Credit Suisse from May to September 2012. This study has been pursued in collaboration with the Global Arbitrage Strategies team.
Quantitative analysis strategy developers use sophisticated statistical and optimization techniques to discover and construct new algorithms. These algorithms take advantage of the short term deviation from the ”fair” securities’ prices. Pairs trading is one such quantitative strategy - it is a process of identifying securities that generally move together but are currently ”drifting away”.
Pairs trading is a common strategy among many hedge funds and banks. However, there is not a significant amount of academic literature devoted to it due to its proprietary nature. For a review of some of the existing academic models, see [6], [8], [11] .
Our focus for this analysis is the study of two quantitative approaches to the problem of pairs trading, the first one uses the properties of co-integrated financial time series as a basis for trading strategy, in the second one we model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem.
This study was performed to show that under certain assumptions the two approaches are equivalent.
Practitioners most often use a fundamentally driven approach, analyzing the performance of stocks around a market event and implement strategies using back-tested trading levels.
We also study an example of a fundamentally driven strategy, using market reaction to a stock being dropped or added to the MSCI World Standard, as a signal for a pair trading strategy on those stocks once their inclusion/exclusion has been made effective.
This report is organized as follows. Section 1 provides some background on pairs trading strategy. The theoretical results are described in Section 2. Section 3
White paper risk management in exotic derivatives trading - ch cie graAugustin Beyot
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses how efficient risk management using techniques like stress testing and limiting risk concentrations could have helped banks avoid such losses from unhedgeable risks in their exotic derivatives positions.
Risk management in exotic derivatives tradingGRATeam
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses the risk management challenges posed by these exotic derivatives, given their complex payoffs. It analyzes the delta, gamma and vega profiles to understand how the risks could change dramatically with market moves like the EUR curve inversion.
The objective of this chapter is to present the main ideas related to option theory
within the very simple mathematical framework of discrete-time models. Essentially,
we are exposing the first part of the paper by Harrison and Pliska (1981).
Cox, Ross and Rubinstein's model is detailed at the end of the chapter in the form
of a problem with its solution.
Option Pricing Models Lecture NotesThis week’s assignment is .docxhopeaustin33688
Option Pricing Models Lecture Notes:
This week’s assignment is quite complex. Keep in mind that the theory behind these pricing models is the important thing to remember for this week’s assignment.
If you feel the need to understand the Black Scholes (BSOPM) model in greater detail, I direct you to and http://en.wikipedia.org/wiki/Black_Scholes.
The models we discuss this week can be used via MS Excel templates, which you will find uploaded to the course content section of our classroom under this week’s folder. There is also an alternative calculator, courtesy of 888options.com located at the Binomial & Black Scholes Calculator link. I strongly encourage you to try these out to get a feel for how the different variables play into the final determination of pricing.
1. Binomial options pricing model
In finance, the binomial options pricing model provides a generalisable numerical method for the valuation of options. The binomial model was first proposed by Cox, Ross and Rubinstein (1979). Essentially, the model uses a "discrete-time" model of the varying price over time of the underlying financial instrument. Option valuation is then via application of therisk neutrality assumption over the life of the option, as the price of the underlying instrument evolves.
Use of the model
The Binomial options pricing model approach is widely used as it is able to handle a variety of conditions for which other models cannot easily be applied. This is largely because the BOPM models the underlying instrument over time - as opposed to at a particular point. For example, the model is used to value American options which can be exercised at any point and Bermudan options which can be exercised at various points.
The model is also relatively simple, mathematically, and can therefore be readily implemented in a software (or even spreadsheet) environment. Although slower than the Black-Scholes model, it is considered more accurate, particularly for longer-dated options, and options on securities with dividend payments. For these reasons, various versions of the binomial model are widely used by practitioners in the options markets.
For options with several sources of uncertainty (e.g. real options), or for options with complicated features (e.g. Asian options), lattice methods face several difficulties and are not practical. Monte Carlo option models are generally used in these cases. Monte Carlo simulation is, however, time-consuming in terms of computation, and is not used when the Lattice approach (or a formula) will suffice. See Monte Carlo methods in finance.
Methodology
The binomial pricing model uses a "discrete-time framework" to trace the evolution of the option's key underlying variable via a binomial lattice (tree), for a given number of time steps between valuation date and option expiration.
Each node in the lattice represents a possible price of the underlying, at a particular point in time. This price evolution forms the basis for t.
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx
IT Careers
The growing demand
Projected Job Growth (2016-26)
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Median Salary
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Number of Jobs (2016)
Questions?
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image2.jpeg
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image4.jpeg
image5.jpeg
image6.jpeg
image7.jpeg
image8.jpeg
Exp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers_Instructions.docx
Grader - Instructions PPT 2022 ProjectExp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers
Project Description:
As the department chair of Information Technology at the college, you often give presentations on IT Careers to potential students. You have been asked to to update the presentation with recent data on the programs you offer at the college that will prepare students for a career in IT.
Steps to Perform:
Step
Instructions
Points Possible
1
Start PowerPoint. Download and open the file named
Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx. Grader has automatically added your last name to the beginning of the filename.
0
2
Insert a Title and Content layout slide after Slide 1. Type
Jobs in IT in the Title Placeholder.
Insert a Trapezoid List SmartArt graphic in the content placeholder. Type the following in the Text pane as first level bullet points. Remove any unneeded bullet points in the Text pane.
Software applications developerInformation security analystComputer systems analystDatabase administratorComputer network architectManagement analyst
20
3
Apply the Moderate Effect SmartArt style. Change the color to Colorful – Accent Colors.
8
4
On Slide 4, insert a two column, seven row table in the content placeholder.
Type the following information into the table:
Row 1: Col 1:
Profession; Col 2:
Salary Per Year
Row 2: Col 1
Software applications developer; Col 2:
$103,560
Row 3: Col 1:
Information security analyst; Col 2:
$95,510
Row 4: Col 1:
Computer systems analyst; Col 2:
$88,270
Row 5: Col 1:
Database administrator; Col 2:
$87,020
Row 6: Col 1:
Computer network architect; Col 2:
$104,650
Row 7: Col 1:
Management analyst; Col 2:
$82,450
12
5
Apply the Light Style 2 – Accent 4 table style.
4
6
Add a new column to the left of column 1. Merge the cells in the new column. Set the cell width of the merged cell .
The Analysis of the Impact of Capital Mobility on Bubbly Episodes Creation in...Andrii Chlechko
The author has developed a stylized experimental model, which is used to analyze the impact of the introduction of capital mobility on the assets’ prices behaviour in the controlled laboratory environment. The introduction of capital mobility is a subject to financial friction in a form of borrowing costs and collateral borrowing. The model is based on SSW-type double-auction market with finite horizon. Current paper analysis two types of markets: one asset market and two assets market. Such a division is crucial for the analysis of resources allocation and subjects decision making. The division of the analyzed population into productive and unproductive investors creates the environment, in which the structure of capital mobility tends to impact the overall market efficiency. The overall combination of presented factors allows the author to analyze the market efficiency based on the deviation of the market traded price over the expected average value of the assets.
http://www.wz.uw.edu.pl/portaleFiles/6133-wydawnictwo-/Rynek_kapitałowy_szanse_2018.pdf
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? ...SYRTO Project
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? - Loriana Pelizzon, Marti Subrahmanyam, Davide Tomio, Jun Uno. June, 5 2014. First International Conference on Sovereign Bond Markets.
Fair valuation of participating life insurance contracts with jump riskAlex Kouam
A C++ based program which prices the fair value of a participating life insurance whereby the underlying follows a Kou process and the insurer's default occurs only at contract's maturity.
Evaluation of options portfolios for exchange rate hedgesnooriasukmaningtyas
In this paper evaluate six exchange rate hedging strategies with financial
options from the OTC market in Colombia. Three hedging strategies for
importers and three for exporters were raised. The coverage for importers
was carried out with the traditional strategy of long call, bull call spread and
bull put spread, the last two correspond to options portfolios. The coverage
for importers was carried out with the traditional strategy of long put, bear
call spread and bear put spread, the last two correspond to options portfolios
to determine the best hedging strategy, the currency price was modeled with
a Wiener process and the VaR for the six covered scenarios was calculated
and compared with the VaR of the uncovered scenario. The results shown by
the six hedging strategies manage to mitigate the exchange risk, but the most
efficient strategies are the traditional ones for both importers and exporters.
This document describes an analysis of the volatility of 24 exchange rates against the US dollar over a 3-year period from 2011 to 2014. The analysis uses statistical methods like GARCH models to estimate volatility, functional data analysis to smooth volatility curves, and principal component analysis and clustering to group currencies based on similar volatility patterns. Key results include identifying 4 groups of currencies with distinct volatility profiles and showing that currencies classified as "fixed" and "floating" exchange rate regimes can exhibit different degrees of volatility.
The document discusses modeling volatility for European carbon markets using stochastic volatility (SV) models. It outlines estimating SV model parameters from market data, re-projecting conditional volatility, and using the re-projected volatility to price options and calculate implied volatilities. The modeling approach involves projecting historical returns, estimating an SV model, and then re-projecting conditional volatility and pricing options based on the estimated model. Parameters are estimated for both the NASDAQ OMX and Intercontinental Exchange carbon markets and model diagnostics are presented.
The document discusses modeling volatility for European carbon markets using stochastic volatility (SV) models. It outlines estimating SV model parameters from market data, simulating conditional volatility distributions, and using these to price options and evaluate market pricing errors. The modeling approach involves projecting returns from an SV model, estimating parameters, and then re-projecting to obtain conditional volatility forecasts for option pricing. Estimated model parameters and implied volatilities from major European carbon exchanges are presented and compared.
Here are the steps to solve this problem using put-call parity:
1) Value of a European call option to buy 1 CAD for $0.85 in 9 months using Black-Scholes:
Call = N(d1)S0 - N(d2)Ke-rft
Where:
S0 = 0.85
K = 0.85
t = 9/12
r_f = 5% = 0.05
σ = implied volatility of CAD/USD
2) Put-call parity:
Put = Call - S0 + Ke-rft
3) Value of a European put option to sell 1 CAD for $0.85 in 9 months:
Investigation of Frequent Batch Auctions using Agent Based ModelTakanobu Mizuta
Recently, the speed of order matching systems on financial exchanges increased due to competition between markets and due to large investor demands. There is an opinion that this increase is good for liquidity by increasing providing liquidity of market maker strategies (MM), on the other hand, there is also the opposite opinion that this speed causes socially wasteful arms race for speed and these costs are passed to other investors as execution costs.
A frequent batch auction (FBA) which reduces the value of speed advantages proposed, however, is also criticized that MM providing liquidity are exposed to more risks, and then they can continue to provide liquidity, then many MM retire, and finally liquidity will be reduced.
In this study we implemented a price mechanism that is changeable between a comparable continuance double auction (CDA) and FBA continuously, and analyzing profits/losses and risks of MM, we investigated whether MM can continue to provide liquidity even on FBA by using an artificial market model.
Our simulation results showed that on FBA execution rates of MM becomes smaller and this causes to reduce liquidity supply by MM. They also suggested that on FBA MM cannot avoid both an overnight risk and a price variation risk intraday, furthermore, it is very difficult that MM is rewarded for risks and continues to provide liquidity. Only on CDA MM is rewarded for risks and continue to provide liquidity.
This suggestion implies that MM that can provide liquidity on CDA cannot continue to provide liquidity on FBA and then many MM retire, finally liquidity will be reduced.
This document provides an overview of liability management in the context of interest rate risk. It discusses key concepts such as active vs passive liability management, interest rate risk, present value, duration, market value at risk, and cost at risk. It proposes four pillars of liability management: minimize bets on markets, avoid negative carry, exploit market inefficiencies, and diversify risks. Several examples and case studies are provided to illustrate concepts related to interest rate derivatives.
This document provides an introduction to bond markets, including:
- A brief history of war bonds and the growth of bond markets since the 1970s.
- Defining bonds as debt instruments issued by borrowers to raise capital from lenders/investors.
- Describing the roles of primary dealers, central banks, and how globalization has impacted bond markets.
- Noting some past difficulties in bond markets like lack of a centralized marketplace and complexity.
- Stating bonds represent debt contracts and have similarities to both bank loans and equity markets.
This document discusses a study analyzing the historical fair value of foreign exchange (FX) options. It examines daily option premium and payout data for various currency pairs and tenors going back to 1995. The study finds that short-dated FX options tend to be overpriced, while long-dated options offer better value. It presents analysis showing the premium, forward point contribution, and actual spot contribution to returns for carry trades. The document also discusses how to calculate option values using Black-Scholes and the costs to include, and considers what results might indicate options are fairly or unfairly priced.
The Validity of Company Valuation Using Dis.docxchristalgrieg
The Validity of Company Valuation
Using Discounted Cash Flow Methods
Florian Steiger
1
Seminar Paper
Fall 2008
Abstract
This paper closely examines theoretical and practical aspects of the widely used discounted
cash flows (DCF) valuation method. It assesses its potentials as well as several weaknesses. A
special emphasize is being put on the valuation of companies using the DCF method. The
paper finds that the discounted cash flow method is a powerful tool to analyze even complex
situations. However, the DCF method is subject to massive assumption bias and even slight
changes in the underlying assumptions of an analysis can drastically alter the valuation
results. A practical example of these implications is given using a scenario analysis.
____________
1
Author: Florian Steiger, European Business School, e-mail: [email protected]
Table of Contents
List of abbreviations ........................................................................................................... i
List of figures and tables ................................................................................................... ii
1 Introduction .................................................................................................................. 1
1.1 Problem Definition and Objective ...................................................................... 1
1.2 Course of the Investigation ................................................................................. 2
2 Company valuation ....................................................................................................... 2
2.1 General Goal and Use of Company Valuation ................................................... 2
2.2 Other Valuation Methods ................................................................................... 3
3 The Discounted Cash Flow Valuation Method ............................................................ 4
3.1 Approach of the Discounted Cash Flow Valuation ............................................ 4
3.2 Calculation of the Free Cash Flow ..................................................................... 5
3.2.1 Cash Flow to Firm and Cash Flow to Equity.................................................. 5
3.2.2 Building Future Scenarios .............................................................................. 6
3.3 The Weighted Average Cost of Capital ............................................................. 6
3.3.1 Cost of Equity ................................................................................................. 7
3.3.2 Cost of Debt .................................................................................................... 8
3.3.3 Summary ......................................................................................................... 9
3.4 Calculation of the Terminal Value ................................................... ...
Solution to Black-Scholes P.D.E. via Finite Difference Methods (MatLab)Fynn McKay
Simple implementable of Numerical Analysis to solve the famous Black-Scholes P.D.E. via Finite Difference Methods for the fair price of a European option.
Statistical Arbitrage
Pairs Trading, Long-Short Strategy
Cyrille BEN LEMRID

1 Pairs Trading Model 5
1.1 Generaldiscussion ................................ 5 1.2 Cointegration ................................... 6 1.3 Spreaddynamics ................................. 7
2 State of the art and model overview 9
2.1 StochasticDependenciesinFinancialTimeSeries . . . . . . . . . . . . . . . 9 2.2 Cointegration-basedtradingstrategies ..................... 10 2.3 FormulationasaStochasticControlProblem. . . . . . . . . . . . . . . . . . 13 2.4 Fundamentalanalysis............................... 16
3 Strategies Analysis 19
3.1 Roadmapforstrategydesign .......................... 19 3.2 Identificationofpotentialpairs ......................... 19 3.3 Testingcointegration ............................... 20 3.4 Riskcontrolandfeasibility............................ 20
4 Results
22
2
Contents

Introduction
This report presents my research work carried out at Credit Suisse from May to September 2012. This study has been pursued in collaboration with the Global Arbitrage Strategies team.
Quantitative analysis strategy developers use sophisticated statistical and optimization techniques to discover and construct new algorithms. These algorithms take advantage of the short term deviation from the ”fair” securities’ prices. Pairs trading is one such quantitative strategy - it is a process of identifying securities that generally move together but are currently ”drifting away”.
Pairs trading is a common strategy among many hedge funds and banks. However, there is not a significant amount of academic literature devoted to it due to its proprietary nature. For a review of some of the existing academic models, see [6], [8], [11] .
Our focus for this analysis is the study of two quantitative approaches to the problem of pairs trading, the first one uses the properties of co-integrated financial time series as a basis for trading strategy, in the second one we model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem.
This study was performed to show that under certain assumptions the two approaches are equivalent.
Practitioners most often use a fundamentally driven approach, analyzing the performance of stocks around a market event and implement strategies using back-tested trading levels.
We also study an example of a fundamentally driven strategy, using market reaction to a stock being dropped or added to the MSCI World Standard, as a signal for a pair trading strategy on those stocks once their inclusion/exclusion has been made effective.
This report is organized as follows. Section 1 provides some background on pairs trading strategy. The theoretical results are described in Section 2. Section 3
White paper risk management in exotic derivatives trading - ch cie graAugustin Beyot
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses how efficient risk management using techniques like stress testing and limiting risk concentrations could have helped banks avoid such losses from unhedgeable risks in their exotic derivatives positions.
Risk management in exotic derivatives tradingGRATeam
- Banks accumulated large positions in structured interest rate derivatives known as spread range accrual products between 2005-2008. These had discontinuous payoffs that depended on reference interest rate spreads staying above certain strike levels.
- In June 2008, there was a sudden and unexpected inversion of the EUR interest rate curve. This caused the gamma exposure of banks' derivatives desks to invert, leading to large losses as the payoffs changed discontinuously.
- The document discusses the risk management challenges posed by these exotic derivatives, given their complex payoffs. It analyzes the delta, gamma and vega profiles to understand how the risks could change dramatically with market moves like the EUR curve inversion.
The objective of this chapter is to present the main ideas related to option theory
within the very simple mathematical framework of discrete-time models. Essentially,
we are exposing the first part of the paper by Harrison and Pliska (1981).
Cox, Ross and Rubinstein's model is detailed at the end of the chapter in the form
of a problem with its solution.
Option Pricing Models Lecture NotesThis week’s assignment is .docxhopeaustin33688
Option Pricing Models Lecture Notes:
This week’s assignment is quite complex. Keep in mind that the theory behind these pricing models is the important thing to remember for this week’s assignment.
If you feel the need to understand the Black Scholes (BSOPM) model in greater detail, I direct you to and http://en.wikipedia.org/wiki/Black_Scholes.
The models we discuss this week can be used via MS Excel templates, which you will find uploaded to the course content section of our classroom under this week’s folder. There is also an alternative calculator, courtesy of 888options.com located at the Binomial & Black Scholes Calculator link. I strongly encourage you to try these out to get a feel for how the different variables play into the final determination of pricing.
1. Binomial options pricing model
In finance, the binomial options pricing model provides a generalisable numerical method for the valuation of options. The binomial model was first proposed by Cox, Ross and Rubinstein (1979). Essentially, the model uses a "discrete-time" model of the varying price over time of the underlying financial instrument. Option valuation is then via application of therisk neutrality assumption over the life of the option, as the price of the underlying instrument evolves.
Use of the model
The Binomial options pricing model approach is widely used as it is able to handle a variety of conditions for which other models cannot easily be applied. This is largely because the BOPM models the underlying instrument over time - as opposed to at a particular point. For example, the model is used to value American options which can be exercised at any point and Bermudan options which can be exercised at various points.
The model is also relatively simple, mathematically, and can therefore be readily implemented in a software (or even spreadsheet) environment. Although slower than the Black-Scholes model, it is considered more accurate, particularly for longer-dated options, and options on securities with dividend payments. For these reasons, various versions of the binomial model are widely used by practitioners in the options markets.
For options with several sources of uncertainty (e.g. real options), or for options with complicated features (e.g. Asian options), lattice methods face several difficulties and are not practical. Monte Carlo option models are generally used in these cases. Monte Carlo simulation is, however, time-consuming in terms of computation, and is not used when the Lattice approach (or a formula) will suffice. See Monte Carlo methods in finance.
Methodology
The binomial pricing model uses a "discrete-time framework" to trace the evolution of the option's key underlying variable via a binomial lattice (tree), for a given number of time steps between valuation date and option expiration.
Each node in the lattice represents a possible price of the underlying, at a particular point in time. This price evolution forms the basis for t.
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx
IT Careers
The growing demand
Projected Job Growth (2016-26)
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Median Salary
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Number of Jobs (2016)
Questions?
image1.jpeg
image2.jpeg
image3.png
image4.jpeg
image5.jpeg
image6.jpeg
image7.jpeg
image8.jpeg
Exp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers_Instructions.docx
Grader - Instructions PPT 2022 ProjectExp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers
Project Description:
As the department chair of Information Technology at the college, you often give presentations on IT Careers to potential students. You have been asked to to update the presentation with recent data on the programs you offer at the college that will prepare students for a career in IT.
Steps to Perform:
Step
Instructions
Points Possible
1
Start PowerPoint. Download and open the file named
Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx. Grader has automatically added your last name to the beginning of the filename.
0
2
Insert a Title and Content layout slide after Slide 1. Type
Jobs in IT in the Title Placeholder.
Insert a Trapezoid List SmartArt graphic in the content placeholder. Type the following in the Text pane as first level bullet points. Remove any unneeded bullet points in the Text pane.
Software applications developerInformation security analystComputer systems analystDatabase administratorComputer network architectManagement analyst
20
3
Apply the Moderate Effect SmartArt style. Change the color to Colorful – Accent Colors.
8
4
On Slide 4, insert a two column, seven row table in the content placeholder.
Type the following information into the table:
Row 1: Col 1:
Profession; Col 2:
Salary Per Year
Row 2: Col 1
Software applications developer; Col 2:
$103,560
Row 3: Col 1:
Information security analyst; Col 2:
$95,510
Row 4: Col 1:
Computer systems analyst; Col 2:
$88,270
Row 5: Col 1:
Database administrator; Col 2:
$87,020
Row 6: Col 1:
Computer network architect; Col 2:
$104,650
Row 7: Col 1:
Management analyst; Col 2:
$82,450
12
5
Apply the Light Style 2 – Accent 4 table style.
4
6
Add a new column to the left of column 1. Merge the cells in the new column. Set the cell width of the merged cell .
The Analysis of the Impact of Capital Mobility on Bubbly Episodes Creation in...Andrii Chlechko
The author has developed a stylized experimental model, which is used to analyze the impact of the introduction of capital mobility on the assets’ prices behaviour in the controlled laboratory environment. The introduction of capital mobility is a subject to financial friction in a form of borrowing costs and collateral borrowing. The model is based on SSW-type double-auction market with finite horizon. Current paper analysis two types of markets: one asset market and two assets market. Such a division is crucial for the analysis of resources allocation and subjects decision making. The division of the analyzed population into productive and unproductive investors creates the environment, in which the structure of capital mobility tends to impact the overall market efficiency. The overall combination of presented factors allows the author to analyze the market efficiency based on the deviation of the market traded price over the expected average value of the assets.
http://www.wz.uw.edu.pl/portaleFiles/6133-wydawnictwo-/Rynek_kapitałowy_szanse_2018.pdf
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? ...SYRTO Project
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? - Loriana Pelizzon, Marti Subrahmanyam, Davide Tomio, Jun Uno. June, 5 2014. First International Conference on Sovereign Bond Markets.
Fair valuation of participating life insurance contracts with jump riskAlex Kouam
A C++ based program which prices the fair value of a participating life insurance whereby the underlying follows a Kou process and the insurer's default occurs only at contract's maturity.
Evaluation of options portfolios for exchange rate hedgesnooriasukmaningtyas
In this paper evaluate six exchange rate hedging strategies with financial
options from the OTC market in Colombia. Three hedging strategies for
importers and three for exporters were raised. The coverage for importers
was carried out with the traditional strategy of long call, bull call spread and
bull put spread, the last two correspond to options portfolios. The coverage
for importers was carried out with the traditional strategy of long put, bear
call spread and bear put spread, the last two correspond to options portfolios
to determine the best hedging strategy, the currency price was modeled with
a Wiener process and the VaR for the six covered scenarios was calculated
and compared with the VaR of the uncovered scenario. The results shown by
the six hedging strategies manage to mitigate the exchange risk, but the most
efficient strategies are the traditional ones for both importers and exporters.
This document describes an analysis of the volatility of 24 exchange rates against the US dollar over a 3-year period from 2011 to 2014. The analysis uses statistical methods like GARCH models to estimate volatility, functional data analysis to smooth volatility curves, and principal component analysis and clustering to group currencies based on similar volatility patterns. Key results include identifying 4 groups of currencies with distinct volatility profiles and showing that currencies classified as "fixed" and "floating" exchange rate regimes can exhibit different degrees of volatility.
The document discusses modeling volatility for European carbon markets using stochastic volatility (SV) models. It outlines estimating SV model parameters from market data, re-projecting conditional volatility, and using the re-projected volatility to price options and calculate implied volatilities. The modeling approach involves projecting historical returns, estimating an SV model, and then re-projecting conditional volatility and pricing options based on the estimated model. Parameters are estimated for both the NASDAQ OMX and Intercontinental Exchange carbon markets and model diagnostics are presented.
The document discusses modeling volatility for European carbon markets using stochastic volatility (SV) models. It outlines estimating SV model parameters from market data, simulating conditional volatility distributions, and using these to price options and evaluate market pricing errors. The modeling approach involves projecting returns from an SV model, estimating parameters, and then re-projecting to obtain conditional volatility forecasts for option pricing. Estimated model parameters and implied volatilities from major European carbon exchanges are presented and compared.
Here are the steps to solve this problem using put-call parity:
1) Value of a European call option to buy 1 CAD for $0.85 in 9 months using Black-Scholes:
Call = N(d1)S0 - N(d2)Ke-rft
Where:
S0 = 0.85
K = 0.85
t = 9/12
r_f = 5% = 0.05
σ = implied volatility of CAD/USD
2) Put-call parity:
Put = Call - S0 + Ke-rft
3) Value of a European put option to sell 1 CAD for $0.85 in 9 months:
Investigation of Frequent Batch Auctions using Agent Based ModelTakanobu Mizuta
Recently, the speed of order matching systems on financial exchanges increased due to competition between markets and due to large investor demands. There is an opinion that this increase is good for liquidity by increasing providing liquidity of market maker strategies (MM), on the other hand, there is also the opposite opinion that this speed causes socially wasteful arms race for speed and these costs are passed to other investors as execution costs.
A frequent batch auction (FBA) which reduces the value of speed advantages proposed, however, is also criticized that MM providing liquidity are exposed to more risks, and then they can continue to provide liquidity, then many MM retire, and finally liquidity will be reduced.
In this study we implemented a price mechanism that is changeable between a comparable continuance double auction (CDA) and FBA continuously, and analyzing profits/losses and risks of MM, we investigated whether MM can continue to provide liquidity even on FBA by using an artificial market model.
Our simulation results showed that on FBA execution rates of MM becomes smaller and this causes to reduce liquidity supply by MM. They also suggested that on FBA MM cannot avoid both an overnight risk and a price variation risk intraday, furthermore, it is very difficult that MM is rewarded for risks and continues to provide liquidity. Only on CDA MM is rewarded for risks and continue to provide liquidity.
This suggestion implies that MM that can provide liquidity on CDA cannot continue to provide liquidity on FBA and then many MM retire, finally liquidity will be reduced.
This document provides an overview of liability management in the context of interest rate risk. It discusses key concepts such as active vs passive liability management, interest rate risk, present value, duration, market value at risk, and cost at risk. It proposes four pillars of liability management: minimize bets on markets, avoid negative carry, exploit market inefficiencies, and diversify risks. Several examples and case studies are provided to illustrate concepts related to interest rate derivatives.
This document provides an introduction to bond markets, including:
- A brief history of war bonds and the growth of bond markets since the 1970s.
- Defining bonds as debt instruments issued by borrowers to raise capital from lenders/investors.
- Describing the roles of primary dealers, central banks, and how globalization has impacted bond markets.
- Noting some past difficulties in bond markets like lack of a centralized marketplace and complexity.
- Stating bonds represent debt contracts and have similarities to both bank loans and equity markets.
This document discusses a study analyzing the historical fair value of foreign exchange (FX) options. It examines daily option premium and payout data for various currency pairs and tenors going back to 1995. The study finds that short-dated FX options tend to be overpriced, while long-dated options offer better value. It presents analysis showing the premium, forward point contribution, and actual spot contribution to returns for carry trades. The document also discusses how to calculate option values using Black-Scholes and the costs to include, and considers what results might indicate options are fairly or unfairly priced.
The Validity of Company Valuation Using Dis.docxchristalgrieg
The Validity of Company Valuation
Using Discounted Cash Flow Methods
Florian Steiger
1
Seminar Paper
Fall 2008
Abstract
This paper closely examines theoretical and practical aspects of the widely used discounted
cash flows (DCF) valuation method. It assesses its potentials as well as several weaknesses. A
special emphasize is being put on the valuation of companies using the DCF method. The
paper finds that the discounted cash flow method is a powerful tool to analyze even complex
situations. However, the DCF method is subject to massive assumption bias and even slight
changes in the underlying assumptions of an analysis can drastically alter the valuation
results. A practical example of these implications is given using a scenario analysis.
____________
1
Author: Florian Steiger, European Business School, e-mail: [email protected]
Table of Contents
List of abbreviations ........................................................................................................... i
List of figures and tables ................................................................................................... ii
1 Introduction .................................................................................................................. 1
1.1 Problem Definition and Objective ...................................................................... 1
1.2 Course of the Investigation ................................................................................. 2
2 Company valuation ....................................................................................................... 2
2.1 General Goal and Use of Company Valuation ................................................... 2
2.2 Other Valuation Methods ................................................................................... 3
3 The Discounted Cash Flow Valuation Method ............................................................ 4
3.1 Approach of the Discounted Cash Flow Valuation ............................................ 4
3.2 Calculation of the Free Cash Flow ..................................................................... 5
3.2.1 Cash Flow to Firm and Cash Flow to Equity.................................................. 5
3.2.2 Building Future Scenarios .............................................................................. 6
3.3 The Weighted Average Cost of Capital ............................................................. 6
3.3.1 Cost of Equity ................................................................................................. 7
3.3.2 Cost of Debt .................................................................................................... 8
3.3.3 Summary ......................................................................................................... 9
3.4 Calculation of the Terminal Value ................................................... ...
1. University
Cattolica
del
Sacro
Cuore
di
Milano
Faculty
of
Banking,
Financial
and
Insurance
Sciences
The
Decline
of
Saras
S.p.a.
Benedetti
Kevin
Matr.
4008804
Course:
Applied
Statistics
for
Finance
Prof.
Iacus
Prof.
Zappa
Accademic
year
2011-‐2012
2. INDEX
Chapter 1 – Preliminary Stock Analysis
1.1 Company Characteristics
1.2 Change Point Analysis
Chapter 2 – Option Valuation
2.1 Valuation of Financial Options: introduction
2.2 The Black & Scholes Model
2.2.1 Comments on B&S Model
2.3 The Monte Carlo Method
Chapter 3 – Lévy Process
3.1 Fast Fourier Transform
3.2 Monte Carlo Approach
Chapter 4 – Greeks Analysis
4.1 Greeks
4.2 Conclusions
3. Chapter 1 - Preliminary Stock Analysis – Saras S.p.a.
1.1 – Abstract
1.2 – Company Characteristics
The Saras Group, whose operations were started by Angelo Moratti in 1962, has approximately
2,200 employees and total revenues of about 11.0 billion Euros as of 31st December 2011. The
Group is active in the energy sector, and is a leading Italian and European crude oil refiner. It sells
and distributes petroleum products in the domestic and international markets, directly and through
the subsidiaries Saras Energia S.A. (in Spain) and Arcola Petrolifera S.p.A. (in Italy). The Group
also operates in the electric power production and sale, through the subsidiaries Sarlux S.r.l. and
Sardeolica S.r.l.. In addition, the Group provides industrial engineering and scientific research
services to the oil, energy and environment sectors through the subsidiary Sartec S.p.A.. Finally, in
July 2011, the Group created a new subsidiary called Sargas S.r.l., which operates in the fields of
exploration and development, as well as transport, storage, purchase and sale of gaseous
hydrocarbons.
Here are the market performance of the stock integrated with an important indicator that are the
volumes:
As we can notice from this picture the stock registered a small decrease in the stock price in the
very first part of the graph but it went up again jast before july 2008. From this point the stock went
down rapidly and it never stopped. Even today the trand of the stock in quite negative. We have to
1
2
3
4
5
SRS.MI [2007-05-21/2012-05-18]
Last 0.74
Volume (millions):
2,239,500
0
10
20
30
40
50
Mag 21
2007
Lug 01
2008
Lug 01
2009
Lug 01
2010
Lug 01
2011
Mag 18
2012
4. say that in the period considered for the analysis the whole world had to face the financial crisis
started in 2008 and the bad situation of this company doesn’t surprise. Deslpite the negative trend
on January 2012 we can observe a n incredibly high value of volumes: during this period, in fact,
there was the probability for the company to be delisted.
1.3 – Change Point Analysis
Given the decline of the stock and the the pattern of the prices during this period of crisis we
observe a “roller coaster” graph. Now we are going through an analysis that could help us to
explain this performance in order to catch the points where a turnaround has been registered.
In this paragraph I want to use an important tool in volatility analysis: the Change Point Analysis.
Considering a process: X = {Xt, 0 ≤ t ≤ T} à dXt = b(Xt)dt + √θσ(Xt)dBt and X0 = x0, 0<θ1,
θ2<∞{Bt, t ≥ 0} à Bt is a Brownian motion and the coefficients are defined and known.
The aim of this analysis is to find a point called τ0(tau0) associated to a parameter called θ(theta).
R-software compute for us this kind of operation and it gives us two values of theta: θ1, the
volatility just before the change point, and θ2, the volatility immediately after the change point.
In our case we are going to analyze a one-year period – from May 20, 2011 to May 20, 2012 – in
order to observe the reaction of the market in a tough span of time: in fact from May 20, 2011 there
were rumors of a probability of delisting, denied on December 1st, 2011.This announcement could
have brought some “good news” fo investors in a definitely not easy period. Therefore we are going
to expect a change point on around this date: from high range of volatility to a more attenuate one.
5.
The picture above confirms our expectations: the value τ0 – the change point – is set on January 10,
2012. In fact after the announcements, where we can observe a steep rise in stock price, we find few
days more of high volatility and then, after January 10 a reduction in volatility.
Analitically speaking here are the R results:
τ0 = 2012-01-10
θ1 = 0.0444403
θ2 = 0.02976848
∆2-1=0.02976848 – 0.0444403 = -0.01467182
0.8
1.0
1.2
1.4
1.6
1.8
S [2011-05-20/2012-05-18]
0.8
1.0
1.2
1.4
1.6
1.8
Last 0.74
Bollinger Bands (20,2) [Upper/Lower]: 0.991/0.730
Mag 20
2011
Ago 01
2011
Ott 03
2011
Dic 01
2011
Feb 01
2012
Apr 02
2012
6. Chapter 2 – Option Valuation
2.1 – Valuation of Financial Options Introduction
A financial option contract gives its owner thr right (but not the obligation) to purchase or sell an
asset at fixed price at some future date. Two distinct kinds of option contracts exist: call options and
put options. A call option gives the owner the right to buy the asset; a put option gives the owner
the right to sell the asset. The most commonly encountered option contracts are options on shares of
stock: a stock option gives the holder the option to buy or sell a share of stock on or before a given
date for a given price.
When a holder of an option enforces the agreement and buys or sells a share of stock at the agreed-
upon price, he is exercising the option. The price at which the holder buys or sells the share of
stock when the option is exercised is called the strike price.
There are two kinds of options. American options, the most common kind, allow their holders to
exercise the option on any date up to and including a final date called the expiration date.
European option allow their holders to exercise the option only on the expiration date.
The price of an European Option derive basically from the difference between the reference price,
the strike price(K), and the value of the underlying asset (S) plus a premium based on the remaining
time until the expiration date of the option
C = max(S – K, 0)
P = max(K –S, 0)
As we can easily deduce from these equations the value of a call option can’t be negative because if
the value drops below zero the owner doesn’t exercise the option at the expiration date. It can be
usefull having a representation of how these options work. Here is the graph of a call option and a
put option with the same strike price:
0.0 0.5 1.0 1.5 2.0
0.00.20.40.60.81.0
Payoff Functions
x
f(x)
Call
Put
7. Nowadays the value of an option is calculated relying on several mathematics models that help us
in predicting the value of an option changes related to changing in the underlying conditions.
In evaluating options analysts should keep clearly in their mind some main conditions: first of all
they surely have to consider the market price of the underlying security, the strike price of the
option and the relationship that stands between them because, as we know, the option price changes
a lot depending on whether the option is in the money or out of the money. Then they have to focus
on the cost of holding the underlying security, the expiration date and the expected volatility of the
underlying security’s price with respect to the life of the option.
2.2 The Black & Scholes Model
Most of the models that, today, are used by analysts all over the world have a common root: the
model devoloped by Fisher Black and Myron Scholes (1973) called the “Black&Scholes Model”
that allows, taking in consideration some assumptions, the pricing of European Call and Put option
using a simple formula.
The assumptions mentioned above are:
1)
The
stock
pays
no
dividends
during
the
option's
life
Most companies pay dividends to their share holders, so this might seem a serious limitation to the
model considering the observation that higher dividend yields elicit lower call premiums. A
common way of adjusting the model for this situation is to subtract the discounted value of a future
dividend from the stock price.
2)
European
exercise
terms
are
used
European exercise terms dictate that the option can only be exercised on the expiration date.
American exercise term allow the option to be exercised at any time during the life of the option,
making american options more valuable due to their greater flexibility. This limitation is not a
major concern because very few calls are ever exercised before the last few days of their life. This
is true because when you exercise a call early, you forfeit the remaining time value on the call and
collect the intrinsic value. Towards the end of the life of a call, the remaining time value is very
small, but the intrinsic value is the same.
3)
Markets
are
efficient
This assumption suggests that people cannot consistently predict the direction of the market or an
individual stock. The market operates continuously with share prices following a continuous Itô
process. To understand what a continuous Itô process is, you must first know that a Markov process
is "one where the observation in time period t depends only on the preceding observation." An Itô
process is simply a Markov process in continuous time. If you were to draw a continuous process
you would do so without picking the pen up from the piece of paper.
8. 4)
No
commissions
are
charged
Usually market participants do have to pay a commission to buy or sell options. Even floor traders
pay some kind of fee, but it is usually very small. The fees that Individual investor's pay is more
substantial and can often distort the output of the model.
5)
Interest
rates
remain
constant
and
known
The Black and Scholes model uses the risk-free rate to represent this constant and known rate. In
reality there is no such thing as the risk-free rate, but the discount rate on U.S. Government
Treasury Bills with 30 days left until maturity is usually used to represent it. During periods of
rapidly changing interest rates, these 30 day rates are often subject to change, thereby violating one
of the assumptions of the model.
6)
Returns
are
lognormally
distributed
This assumption suggests, returns on the underlying stock are normally distributed, which is
reasonable for most assets that offer options.
7) the stock price follows a geometric Brownian motion with constant drift and volatility.
This part will be widley covered in the “comments on B&S” paragraph
Suppose that all of the assumptions above are verified: in this case we can plainly calculate the
price of an option using the exact formula of B&S.
Here are the Call and the Put formula, respectively:
Call
price:
Put
price:
Where:
9. and:
What we are going to do now is try to compute the price of a Call and a Put option on “Saras S.p.a”
using the Black&Scholes formula in order to verify if the results of the model are plausible with
respect to the real market value of the same options.
First of all we look on a financial web site (Yahoo.fianance, Google Finance etc.) focusing on data
we need to proceed in our calculations. We have to remember that the expiration date is going to be
expressed as effective trading days (252). Here are the parameters:
S0= 0.75 K=0.72 T=20/252 r=0.005 σ=?
At this point of our calculations, unfortunately, we have a missed value: the volatility.
This parameter is not directly observable on the market so we have to find it out by ourselves and
using R it is quite immediate to obtain that the historical volatility, in the period from 2011-01-05 to
2012-01-05, is equal to 0.54220241
.
SARAS S.p.a
MKT “C” B&S”C” MKT”P” B&S”P”
0.039 0.06149544 0.029 0.03120970
Looking at the results we can easily deduce that both Call and Put options prices of Saras S.p.a.
computed using Black&Scholes formula are higher than the market prices of the same options.
This could be caused by the value of the volatility used in the computation: in fact there is the
possibility that our value is higher than the one used by analysts in the market.
These results tell us that the future expected volatility is lower than the historical volatility of the
past year (the one we used) and this reveals that there is a lower chance for the option to be in the
money at the maturity date reflecting the possibility of lower oprion prices.
Now our goal is to understand if our expectations about the value of the historical volatility are
confirmed and see how much is the difference between the historical volatility and the implied one.
R software gives us a function that can do this for us and the results are:
1
I wanted to be sure about the historical volatility so I checked it out using the Call-Parity equation
and it has been confirmed.
10. Implied Volatility for a CALL Historical Volatility
σ = 0.2468025 σ = 0.5422024
Implied Volatility for a PUT
σ = 0.5443167
A we can see from these results the implied volatility used by analysts to price the options are
different from the historical in the CALL option case and this is reflected in the difference between
the market price and the one computed using the B&S formula.
On the other hand the implied volatility used in pricing the PUT is extremely similar to the
historical one; in fact the market price is 0.029 against the B&S one of 0.031.
Looking at this graph, that represents the trend of the Saras stock prices over the last year, we can
see some indicators tant could help us with the volatility pattern.
In order to do so some two technical tools have been included in this chart and they will surely help
our comprehension of what is the overall situation. These tools are: Bollinger Bands (BBands) and
the Average True Range(ATR).
These indicators, combined together, are very common in fincance in order to predict the inversion
of trend of a security (a stock in our case).
0.8
1.0
1.2
1.4
1.6
1.8
S [2011-05-05/2012-05-04]
Last 0.88
Bollinger Bands (20,2) [Upper/Lower]: 0.967/0.846
0.04
0.05
0.06
0.07
0.08
0.09
0.10
Mag 05
2011
Lug 01
2011
Set 01
2011
Nov 01
2011
Gen 02
2012
Mar 01
2012
Mag 02
2012
11. The Average True Range has the aim of calculate prices volatility as the breadth of their
fluctuations. This mechanism is based on the idea that, during a turnaround, the volatility assume
extreme values (high or low).
On the other hand we have the Bollinger Bands that are based on the volatility calculation as well
but there is a superior band and an inferior one so that we can have an idea of what the volatility
range is. From an operating point of view the Bands give us signals of buying or selling when these
conditions occure:
-‐ when the price graph goes out of the upper band and it goes in again, this is a selling sign;
this could represent an increase in price followed by an adjustment.
-‐ When the same thing happens with respect to the lower band, we have a buying sign; this
means that the price has gone down very quickly up to the point of turnaround.
Concerning Saras S.p.a. we can see some of these situations. For example we see that on August
2011 the graph crossed the lower band meaning a strong decrease in price in fact in this period we
registered the peak of the Euro debt crisis. We can see that even from the volatility graph that on
that date had a steep rise. The price went down up to the point of turnaround, crossing again the
lower band, giving a signal of strong buying of the stock.
Conversely, on December 2011, we can see the same process that led to a steep rise in the stock
price in according to the volatility graph that registered a strong increase. In this case the stock price
went up and, just after having crossed the upper line, went down meaning that we were in front of a
turnaround and people were selling their shares.
Looking at the very last part of the chart we can see that, in these days, the stock is having a
negative trend and this is the reason why the implied volatility of the put option is higher than the
call one telling us that the market expectations for the near future is still a down-trend.
2.2.1 – Comments
The Black&Scholes formula, the one we used before to price Saras S.p.a. options, doesn’t match
very well with the real world we live in.
That’s why, as we have seen in the previous paragrph, is based on some assumptions that are
impossible to notice in real circumstances: in particular what we are going to prove now is the
soundness of the assumption that says stock price follows a geometric Brownian motion with
constant drift and constant volatility. In order to understand better what we are going to talk about
let’s see, first, what a geormetric Brownian motion actually is.
One of the most important assumption of the B&S model is that stock prices follow a Normal
distributed process and this processi s known as the geometric Brownian motion. It defined as
follows:
dSt = µSt dt + σSt dWt
where:
σ is the volatility and it is assumed constant.
12. µ is the expected return.
Wt is Wiener Process that is the stochastic component of the process. In order to compute our
demonstration we are not going to use the returns of the stock. Instead, we are going to use the
logarithm of the returns (more reliable) given by the ratio: returns in time t over returns in time t-1.
Here is the expression:
log.returns = returnst / returnst-1
Gen 02
2007
Gen 02
2008
Gen 02
2009
Gen 04
2010
Gen 03
2011
Gen 02
2012
-0.100.000.100.20
log.returns
14. These graphs are a very clear demonstration of what we were looking for. The density function of
our company are not properly ditributed and we are going to comment some indicators that tell us
why: first of all, looking at the second graph(the density function), we can easily see that the tails of
the function are not linear adn well defined as farther we move from central values. This fact
underline the fact that the price does not follow a geometric Brownian motion so that the
assumption does not hold. Finally, the third picture, should give us the explanation of why our
prices does not follow a Normally distributed function. If we look at it we can see a straight line
indicating the path that our prices should follow to be Normally distributed and the path they
actually follow. Concerning central values they seem to be as the ideal funtion wants them to be but
if we move from central value we notice that they go astray.
2.2.2 – Volatility Smiles
As we wrote before, B&S formula assesses that prices follow a normally distributed trend with
constant volatiltiy. In this part we are to verify this last statement.
Here are some options (call and put) with the same expiration date:
Saras S.p.a.
Expiration June 15, 2012
CALL PUT
Mkt Strike(K) Mkt
0.0435 0.74 0.03
0.0415 0.76 0.037
0.032 0.78 0.047
0.0205 0.8 0.051
0.008 0.85 0.086
0.003 0.9
0.0005 0.95
0.0005 1
0.0005 1.05
For semplicity we show and comment only the graph related to Call Option.
15.
As we can immediatly see the stock prices are not characterized by constant volatility and, instead,
it changes as the strike price changes.
The volatility seems to follow a particular path that, as analysts call it, can be similar to a “smile”.
This kind of graph is called “Volatility Smile”. The reason of this trend is related to the fact that
values are high when the option is deeply in, or conversely out, of the money and decrease when the
option is near to the “at the money point”.
2.3 – The Monte Carlo Method
As we have already mentioned, the Black&Scholes model is used all over the world for pricing
options but our paper shows that the assumptions it is based on are quite unrealistic and through our
calculations we disproved them.
Now we are going to challenge another method, the Monte Carlo Method, based on a simulation: a
random generation of thousand of combination of prices . The successive step is going to be the
calculation of the payoff of the option for each simulation; the discounted results will be the price of
the option we are looking for.
0.75 0.80 0.85 0.90 0.95 1.00 1.05
0.400.450.500.55
Volatility smile SARAS S.P.A
K
smile
!^
16. In this part of the paper we want to compare two different prices of the same option: we have
already calculated one of these two prices that is generated by B&S model. The second one is going
to be generated by the Monte Carlo method. Then we are going to discuss our results.
As Monte Carlo simulation is based on repeated price generation we are goint to take different
scenarios characterised by different number of simulation (1000, 10000, 100000, 1000000) and
what we expect is that the higher are repetitions the more precise would be our price according to
the market one.
SARAS S.p.a.
CALL M PUT
0.05976969 1000 0.03035832
0.06110445 10000 0.03120731
0.06154876 100000 0.0311551
0.06154721 1000000 0.03115751
As we expected the accuracy of prices with respect to ones calculated by B&S is as higher as the
number of the simulations increase.
This is the main characteristic of the Monte Carlo simulation based on the “large number law” and
it is plain also using a function of R software called “speed of convergence” that show us
graphically the characteristic we’ve mentioned above.
To simplify our calculations, our analysis on convergency is going to be done only on one option:
the characteristics of this option are S0=0.75 and the strike price(K)= 0.72. The interest rate is, as
for other operations, is the interest rate of deutsch bank.
17.
Chapter 3 – Lévy Process
In probability theory, a Lévy process, named after the French mathematician Paul Lévy, is a
stochastic process that starts at 0, admits càdlàag(continue à droite, limitée à gauche) modification
and has "stationary independent increments" — this phrase will be explained below. It is a
stochastic analog of independent and identically distributed random variables, and the most well
known examples are the Wiener process and the Poisson process.
It is defined as follows:
A stochastic process X = {Xt : t ≥ 0} is said to be a Lèvy process if,
Speed of Convergency - Saras S.p.a.
MC replications
MCprice
10 100 200 500 1000
18. 1) X0 = 0 almost surely
2) Indipendent increments: For any 0 ≤ t1 < t2 < … < tn < ∞, Xt2 – Xt1, Xt3 – Xt2, … , Xtn – Xtn-1
are indipendent.
3) Stationary increments: for any s < t, Xt – Xs is equal in distribution to Xt-s
4) t -> Xt is almost surely right continuous with left limits.
In order to better understand what is wrong in the B&S Model we are going to discuss and represent
graphically the problem. First we have to say that distribustions are not normal:
Looking at the graph it is evident that the distribution of the logarithm of the returns is definitely
different from a normal distributed function (as we have already computed for the B&S Model).
Our goal is, now, find a new model which should not be based on geometric Brownian motion like
the B&S model. Well, a solution to our problem could be represented by Lévy proccesses.
Using R-software we are able to compute some Lévy processes and to plot them giving us a sketch
of what could be a solution to our problem:
-0.1 0.0 0.1 0.2
02468101214
density.default(x = Ret.Saras)
N = 253 Bandwidth = 0.008547
Density
19.
In the above we can see:
Picture a) the NORMAL parameter estimation.
MEAN:-0.003379408 SD:0.036455639
Picture b) the NORMAL INVERSE GAUSSIAN parameter estimation
ALPHA:28.375193345 BETA:1.564509140
DELTA:0.036703176MU:-0.005406176
Picture c) the HYPERBOLIC parameter estimation
ALPHA:43.912065188 BETA:1.505010969
DELTA:0.016400880MU:-0.005299175
Picture d) the GENERALIZED HYPERBOLIC parameter estimation
ALPHA:3.813755900 BETA:2.074031129
DELTA:0.057400423MU:-0.006110047
-0.10 -0.05 0.00 0.05 0.10
-2-1012
x
logf(x)
NORMAL: Parameter Estimation
-0.10 0.00 0.05 0.10 0.15 0.20
-4-202
x
logf(x)
NIG Parameter Estimation
-0.10 0.00 0.05 0.10 0.15 0.20
-6-4-202
logf(x)
HYP Parameter Estimation
-0.10 0.00 0.05 0.10 0.15 0.20
-0.50.51.5
logf(x)
GH Parameter Estimation
20. LAMBDA:-2.229897013
Once we have introduced what the Lévy process is we have to make another important assumption:
it says that Lévy markets, even if we condider simpliest one, are not complete.
Now we can proceed in pricing an option and, in this particular case, this operation can follow two
ways:
1) the Fast Fourier Transform
2) the Monte Carlo Approach
In order to simplify my processes we chose to proceed just for the first method giving a sketch of
theory for the second one.
3.1 – Fast Fourier Transform
A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier
transform (DFT) and its inverse. There are many distinct FFT algorithms involving a wide range of
mathematics, from simple complex-number arithmetic to group theory and number theory. A DFT
decomposes a sequence of values into components of different frequencies. This operation is useful
in many fields but computing it directly from the definition is often too slow to be practical. An
FFT is a way to compute the same result more quickly: computing a DFT of N points in the naive
way, using the definition, takes O(N2
) arithmetical operations, while an FFT can compute the same
result in only O(N log N) operations. For example, corcerning the gepmetric Brownian motion, the
characteristic function of Zt is: φ(u)= exp( iu(µ – 1/2 σ2
) - σ2
u2
/2) where φ is known, the proce of an
option can be approximated to this equation:
CT(k) ≈ e-α
k
/π ∑e-iv
j
k
ψT(vj)η, where vj = η(j-1) and k = logK.
The constant alpha is considered as the dampening factor and it is usally equal to one. This model
can we easily used on R and the results we obtained are:
B&S Price FFT Price ∆
0.06149541 - 0.05932576 = 0.00216965
3.2 – The Monte Carlo Approach
The first step we have to take is identifying the distribution of the returns. Then we just have to
simulate the patterns of the stochastic process and apply the payoff function to the final value.
ST = S0eZ
T
Chapter 4 – Greeks Analysis and Conclusions
4.1 – Greeks
In mathematical finance, the Greeks are the quantities representing the sensitivities of the price of
derivatives such as options to a change in underlying parameters on which the value of an
21. instrument or portfolio of financial instruments is dependent. The name is used because the most
common of these parameters are often denoted by Greek letters. Collectively these have also been
called the risk sensitivities, risk measures or hedge parameters.
The Greeks are vital tools in risk management. Each Greek measures the sensitivity of the value of
a portfolio to a small change in a given underlying parameter, so that component risks may be
treated in isolation, and the portfolio rebalanced accordingly to achieve a desired exposure.
The Greeks in the Black–Scholes model are relatively easy to calculate, a desirable property of
financial models, and are very useful for derivatives traders, especially those who seek to hedge
their portfolios from adverse changes in market conditions. For this reason, those Greeks which are
particularly useful for hedging delta, theta, and vega are well-defined for measuring changes in
Price, Time and Volatility. Although rho is a primary input into the Black–Scholes model, the
overall impact on the value of an option corresponding to changes in the risk-free interest rate is
generally insignificant and therefore higher-order derivatives involving the risk-free interest rate are
not common.
The most common of the Greeks are the first order derivatives: Delta, Vega, Theta and Rho as
well as Gamma, a second-order derivative of the value function. The remaining sensitivities in this
list are common enough that they have common names, but this list is by no means exhaustive.
FIRST ORDER GREEKS
Delta
Delta, , measures the rate of change of option value with respect to changes in the underlying
asset's price. Delta is the first derivative of the value of the option with respect to the underlying
instrument's price .
For a vanilla option, delta will be a number between 0.0 and 1.0 for a long call (and/or short put)
and 0.0 and −1.0 for a long put (and/or short call) – depending on price, a call option behaves as if
one owns 1 share of the underlying stock (if deep in the money), or owns nothing (if far out of the
money), or something in between, and conversely for a put option.
Vega
22. Vega measures sensitivity to volatility. Vega is the derivative of the option value with respect to the
volatility of the underlying asset.
Vega can be an important Greek to monitor for an option trader, especially in volatile markets,
since the value of some option strategies can be particularly sensitive to changes in volatility. The
value of an option straddle, for example, is extremely dependent on changes to volatility.
Theta
Theta, θ, measures the sensitivity of the value of the derivative to the passage of time: the "time
decay.”
The mathematical result of the formula for theta is expressed in value per year. By convention, it is
usual to divide the result by the number of days in a year, to arrive at the amount of money per
share of the underlying that the option loses in one day. Theta is almost always negative for long
calls and puts and positive for short calls and puts. An exception is a deep in-the-money European
put. The total theta for a portfolio of options can be determined by summing the thetas for each
individual position.
The value of an option can be analysed into two parts: the intrinsic value and the time value. The
intrinsic value is the amount of money you would gain if you exercised the option immediately,
while the time value is the value of having the option of waiting longer before deciding to exercise.
Rho
Rho, , measures sensitivity to the interest rate: it is the derivative of the option value with respect
to the risk free interest rate (for the relevant outstanding term).
Except under extreme circumstances, the value of an option is less sensitive to changes in the risk
free interest rate than to changes in other parameters. For this reason, rho is the least used of the
first-order Greeks.
Rho is typically expressed as the amount of money, per share of the underlying, that the value of the
option will gain or lose as the risk free interest rate rises or falls by 1.0% per annum (100 basis
points).
Lambda
23. Lambda, , omega, , or elasticity is the percentage change in option value per percentage
change in the underlying price, a measure of leverage, sometimes called gearing.
SECOND ORDER GREEKS
Gamma
Gamma, , measures the rate of change in the delta with respect to changes in the underlying price.
Gamma is the second derivative of the value function with respect to the underlying price. All long
options have positive gamma and all short options have negative gamma. Gamma is greatest
approximately at-the-money (ATM) and diminishes the further out you go either in-the-money
(ITM) or out-of-the-money (OTM).
When a trader seeks to establish an effective delta-hedge for a portfolio, the trader may also seek to
neutralize the portfolio's gamma, as this will ensure that the hedge will be effective over a wider
range of underlying price movements. However, in neutralizing the gamma of a portfolio, alpha (the
return in excess of the risk-free rate) is reduced.
Thanks to R, calculating the coefficients of Greeks on Saras Call options is quite easy and using a
simple function here are the results we obtained:
SARAS S.p.a
As the Underlying Stock Price Changes - Delta and Gamma
Delta measures the sensitivity of an option's theoretical value to a change in the price of the
underlying asset. It is normally represented as a number between -1 and 1, and it indicates how
much the value of an option should change when the price of the underlying stock rises by one euro.
As an alternative convention, the delta can also be shown as a value between -100 and +100 to
show the total euro sensitivity on the value 1 option, which comprises of 100 shares of the
underlying.
Call options have positive deltas and put options have negative deltas. At-the-money options
generally have deltas around 50. Deep-in-the-money options might have a delta of 80 or higher,
while out-of-the-money options have deltas as small as 20 or less.
In our case if we multiply the value of Delta we obtain: 0.6354121*100=63.54121.
Delta 0.6354121
Gamma 3.279769
Theta 0.06149541
Vega 0.07938833
Lambda 7.749506
Rho 0.03294156
24. The value can be considered standing around 50 so we are describing an option that stands just
beyond the “at the money” zone but, at the same time, it is not in “in the money” zone.
Furthermore, if the underlying price changes by 1 euro the related variation in the option proce is
going to be almost 0.64.
Another thing we are interested in is how delta may change as the stock proce moves: Gamma
measures the rate of change in the delta for each one-point increase in the underlying asset. It is a
valuable tool in helping you forecast changes in the delta of an option or an overall position.
Gamma will be larger for the at-the-money options, and gets progressively lower for both the in-
and out-of-the-money options. Unlike delta, gamma is always positive for both calls and puts.
In our case this indicator helps us to better understand what kind of option we are dealing with in
fact as we said above lower values of gamma indicates a “in the money” option (almost 3.28).
Besides, it tells us that for one point our delta is going to change by 3.28.
Changes in Volatility and the Passage of Time - Theta and Vega
Theta is a measure of the time decay of an option, the euro amount that an option will lose each day
due to the passage of time. For at-the-money options, theta increases as an option approaches the
expiration date. For in- and out-of-the-money options, theta decreases as an option approaches
expiration.
Theta is one of the most important concepts for a beginning option trader to understand, because it
explains the effect of time on the premium of the options that have been purchased or sold. The
further out in time you go, the smaller the time decay will be for an option. If you want to own an
option, it is advantageous to purchase longer-term contracts. If you want a strategy that profits from
time decay, then you will want to short the shorter-term options, so that the loss in value due to time
happens quickly.
In our case theta is low (almost 0.06) indicating that our option approaches the expiration and it
actually does because the expiration of the option we considered in the calculation expires within a
month(June 15, 2012). Since the expiration date is not far this indicator tells us that the amount
money we are going to loose up to the expiration is small.
Vega measures the sensitivity of the price of an option to changes in volatility. A change in
volatility will affect both calls and puts the same way. An increase in volatility will increase the
prices of all the options on an asset, and a decrease in volatility causes all the options to decrease in
value.
However, each individual option has its own vega and will react to volatility changes a bit
differently. The impact of volatility changes is greater for at-the-money options than it is for the in-
or out-of-the-money options. While vega affects calls and puts similarly, it does seem to affect calls
more than puts. Perhaps because of the anticipation of market growth over time.
Since we are analyzing an “in the money” option even vega has a low value indicating a small
impact of volatility.
25. Changes with respect to interest rate - Rho
Since it measures the sensitivity with respect to the interest rate we are going to multiply by 100 the
value we obatained on R: 0.03294156*100=3,294156. This value in the gain of our option related to
a variation of 1.0% of the interest rate.
Elasticity - Lambda
Finally we are going to discuss the only second order greek that gives us a measure of leverage. In
our case it is 7.749506 and this is the percentage variation (almost 7.75%) in our option per
percentage change in the underlying asset value.
4.2 - Conclusions
To be honest working on this paper actually helped me to better understand the reality of the
financial market but, first of all, the reality of an incredibly important company as Saras within the
financial market. My technical skills were low at the beginning of the process but this lack helped
me consolidating my theory and my practical skills. Concerning the results obtained I have to say
that I am satisfied. According to these results the company is facing a tough crisis period and the
stock prices reflect it. If we look at the options value, the price of a put option still worths more than
a call indicating the trend is not going to have turnaround. The differences in prices is not very
consistent due to the different methods we have implented: for example Lévy process should have
given a price improving the Black & Scholes formula based on the geometric Brownian motion but
the difference between the two prices is very low. Afterall, going through my analysis I found one
of the most powerfull company in the field of refining facing the difficulties in a very bad way.
Besides, the problems are not coming only from financial markets in facts the company is working
to fix the problem of its employers’ death while on working . The overall situation is very clear and
it tells that this company is going down, the stock price registered a decreasing trend and I don’t
think is going to stop.